A Flexible Neural Network-Based Tool for Package Second Level Interconnect Modeling
2023 IEEE 32nd Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)(2023)
摘要
This paper introduces a neural network (NN)-based practical design tool for quick assessment of package second level interconnects (SLIs) at the earlier design stages. The study addresses the well-known computational cost problem of data generation and training processes of NN implementation by proposing a flexible model approach, where the SLI geometry is divided into several building blocks, for which a separate NN model was trained. The NNs take geometrical parameters as inputs and return the complex S-parameter matrices as outputs. The electrical performance of the entire SLI geometry is obtained by cascading the S-paramaters of the building blocks.
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关键词
Neural network,high-speed I/O,S-parameters,packaging,second level interconnect
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